arXiv cs.AI· Haichuan Tan, Bowen Gao, Tao Huang, Jiaxin Tan, Xin Hong, Baoguo Yao, Yifei Wang, Zhichao Deng, Hanji Du, Dapeng Jiang, Yiheng Li, Weiwei Ma, Xin Ma, Yuqi Shi, Zixiang Wang, Kelin Wu, Tianxiang Wu, Wenxuan Xie, Jie Zang, Huiyu Zhang, Mojia Zhang, Yuying Zhang, Ya-Qin Zhang, Wei-Ying Ma, Yanyan Lan·· 4 小时前精选AI 评分63
PharmAgents2 论文提出端到端自主药物发现系统
PharmAgents2: Towards Autonomous End-to-End Drug Discovery
AI 导读
arXiv 论文 arXiv:2611.07653 提出 PharmAgents2，一个结合知识增强决策与大规模并行探索的端到端自主药物发现系统。
推荐理由
论文给出 PharmAgents2 自主药物发现系统的设计与真实实验验证，读者可了解多智能体科研决策在药物研发中的落地方式。
正文 · 原文
Authors:Haichuan Tan, Bowen Gao, Tao Huang, Jiaxin Tan, Xin Hong, Baoguo Yao, Yifei Wang, Zhichao Deng, Hanji Du, Dapeng Jiang, Yiheng Li, Weiwei Ma, Xin Ma, Yuqi Shi, Zixiang Wang, Kelin Wu, Tianxiang Wu, Wenxuan Xie, Jie Zang, Huiyu Zhang, Mojia Zhang, Yuying Zhang, Ya-Qin Zhang, Wei-Ying Ma, Yanyan Lan
Abstract:Drug discovery requires continuous scientific decision-making across a vast research space. We introduce PharmAgents2, an autonomous system for end-to-end drug discovery that combines knowledge-enhanced decision-making with organized large-scale exploration. Paper2Skill extracts transferable scientific decision knowledge from public sources and, together with expert-developed skills, supports research decisions. Self-Scaling Discovery expands exploration through parallel agents inheriting research context and decision-making capabilities across targets, scientific hypotheses, and discovery strategies. We introduce PharmAgents-Bench v0.1, comprising 121 questions across nine decision scenarios and three end-to-end tasks assessed with expert-defined process rubrics. PharmAgents2 outperforms evaluated baselines in most decision categories and achieves the highest process scores on all three end-to-end tasks. In three real-world studies, autonomous investigations generated findings subsequently supported by human-executed experiments. For LpxH, PharmAgents2 hypothesized a previously unreported allosteric site and screened compounds against it, identifying a novel inhibitor scaffold with experimentally confirmed inhibitory activity and binding. For JAK1, it prioritized a covalent inhibition strategy and nominated compounds with experimentally demonstrated activity. Starting from a known bioactive molecule, Self-Scaling Discovery explored candidate targets across the proteome and identified a new binding target, with the interaction subsequently confirmed experimentally. PharmAgents2 represents a prototype of a virtual pharmaceutical company, marking a step toward autonomous end-to-end drug discovery.
| Subjects: | Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2611.07653 [cs.AI] |
| (or arXiv:2611.07653v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.07653 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Haichuan Tan [view email]
[v1]
Thu, 5 Nov 2026 08:39:19 UTC (19,231 KB)
来源：arXiv cs.AI · arxiv.org